Demo Data from VLDB 2026 / reviewers in the wild / expert
Carl Johnson
dblp:44/10570
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1980
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Theoretical computer science
1 paper |
Information theory · 100% | |
| Artificial intelligence
1 paper |
Probabilistic and Bayesian machine learning · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › signal processing › filtering
nonlinear filtering |
0.0 | 1 | 1980 | Reproducing probability density classes on Lie groups with application to discrete-time filtering (Corresp.) · IEEE Trans. Inf. Theory 1980 |
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › bayesian inference
bayesian filtering |
0.0 | 1 | 1980 | Reproducing probability density classes on Lie groups with application to discrete-time filtering (Corresp.) · IEEE Trans. Inf. Theory 1980 |
Methods — techniques the papers use, named apart from their topics
probability density propagation · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1980 | Reproducing probability density classes on Lie groups with application to discrete-time filtering (Corresp.)abstractA general approach to discrete-time filtering for dynamical systems evolving in a Lie group is presented. The most general filtering solution is given by the signal probability density function conditioned on the measurements, which for nonlinear systems is usually nonparametric. Carl Johnson, Edwin B. Stear |
IEEE Trans. Inf. Theory | 1 |